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Multiple Markov Chains Monte Carlo Approach for Flow Forecasting in Porous Media*

V. Ginting, F. Pereira, A. Rahunanthan
2012 Procedia Computer Science  
A Bayesian approach using Markov Chain Monte Carlo (MCMC) methods is well suited for reconstructing permeability and porosity fields.  ...  The parallel computation of multiple MCMCs can substantially reduce computation time and can make the framework more suitable to subsurface flows.  ...  The flow simulator computation time and the sequential nature of MCMC simulation limit the posterior exploration in a practical period of time.  ... 
doi:10.1016/j.procs.2012.04.076 fatcat:4z2wpueza5djnckpzsaihx6nga

Parallelization and performance characterization of protein 3D structure prediction of Rosetta

Wenlong Li, Tao Wang, E. Li, D. Baker, Li Jin, S. Ge, Yurong Chen, Yimin Zhang
2006 Proceedings 20th IEEE International Parallel & Distributed Processing Symposium  
The asynchronous interactive scheme, with the adaptation of dynamic solution interaction, outperforms the other two, delivering much faster convergence speed and better solution quality.  ...  to parallelize Rosetta.  ...  better solution than single Markov chain search scheme.  ... 
doi:10.1109/ipdps.2006.1639296 dblp:conf/ipps/LiWLBJGCZ06 fatcat:nqqrj4ocwfbspbwz5g7s5mtmrq

Hydrogeological model selection among complex spatial priors

C. Brunetti, M. Bianchi, G. Pirot, N. Linde
2019 Water Resources Research  
We consider a smallscale tracer test for which the spatial distribution of hydraulic conductivity impacts multilevel solute concentration data observed along a 2-D transect.  ...  Hydrogeological field studies rely often on a single conceptual representation of the subsurface.  ...  Niklas Linde thanks Arnaud Doucet for initially suggesting the use of thermodynamic integration.  ... 
doi:10.1029/2019wr024840 fatcat:tt3o76mrkjd67mhj6px6e3kbjm

Stochastic inverse method for estimation of geostatistical representation of hydrogeologic stratigraphy using borehole logs and pressure observations

Dylan R. Harp, Velimir V. Vesselinov
2010 Stochastic environmental research and risk assessment (Print)  
Along with an unconstrained Markov-chain model, simplifying constraints to the Markov-chain model, including (1) proportionally-random and (2) symmetric spatial correlations, are evaluated in the stochastic  ...  Markov-chain models.  ...  The authors are grateful for the ideas provided by Zhenxue Dai during the early stages of development of the presented methodologies.  ... 
doi:10.1007/s00477-010-0403-2 fatcat:diwguk6ibbbydmwdbysj3vdeye

Probabilistic inference of multi-Gaussian fields from indirect hydrological data using circulant embedding and dimensionality reduction

Eric Laloy, Niklas Linde, Diederik Jacques, Jasper A. Vrugt
2015 Water Resources Research  
It is also found to outperform the method of anchored distributions (MAD) for the same computational budget.  ...  Our method is shown to be more efficient than sequential Gibbs sampling (SGS) for the considered case study, particularly when implemented on a distributed computing cluster.  ...  We are grateful to Thomas Mejer Hansen and coworkers for sharing online their mGstat and SIPPI toolboxes. We also like to thank Rouven K€ unze for providing us with the MaFloT simulator.  ... 
doi:10.1002/2014wr016395 fatcat:o3paekmoyfaippfofzetfvd5bm

Research Status of and Trends in 3D Geological Property Modeling Methods: A Review

Yuyang Liu, Xiaowei Zhang, Wei Guo, Lixia Kang, Jinliang Gao, Rongze Yu, Yuping Sun, Mao Pan
2022 Applied Sciences  
It is shown that considering the numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model for simulations in petroleum exploration and development.  ...  For the interpolation methods aspect, most geological phenomena are nonstationary, to simulate various types of reservoirs; the main development trends are increasing geological constraints and reducing  ...  We express our sincere thanks for American Journal Expert (AJE) for their linguistic assistance. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12115648 fatcat:gc55zlosnfcxha4pnw6vfngyaa

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Eric Laloy, Romain Hérault, John Lee, Diederik Jacques, Niklas Linde
2017 Advances in Water Resources  
unconditional geostatistical simulation of a channelized prior model.  ...  For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR).  ...  We thank Mike Swarbrick Jones for sharing his codes (, one of which served as a starting point for our devised VAECNN.  ... 
doi:10.1016/j.advwatres.2017.09.029 fatcat:pwefgaldrjdvpbwn4x4snxw5ni

Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

Eric Laloy, Romain Hérault, Diederik Jacques, Niklas Linde
2018 Water Resources Research  
A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC  ...  Several 2D and 3D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours.  ...  Acknowledgments Python codes of the proposed 2D/3D SGAN-based simulation and inversion approaches are available from the first author (and will be made available on https://github. com/elaloy).  ... 
doi:10.1002/2017wr022148 fatcat:fc52n56iqffbbfx3jkj332ulqm

Bayesian inversion of seismic attributes for geological facies using a Hidden Markov Model

Muhammad Atif Nawaz, Andrew Curtis
2016 Geophysical Journal International  
Markov chain Monte-Carlo (McMC) sampling generates correlated random samples such that their distribution would converge to the true distribution only as the number of samples tends to infinity.  ...  We propose a more efficient method for Bayesian inversion of categorical variables, such as geological facies that requires no sampling at all.  ...  Previous work in the field of petroleum geoscience used Markov-chains and Hidden Markov Models for inversion of seismic data for geological facies (e.g., Larsen et al. 2006; Ulvmoen & Omre 2010; Ulvmoen  ... 
doi:10.1093/gji/ggw411 fatcat:jmfbnkd7nbee5f5vx6ocly6bma

Modeling Fine-Scale Geological Heterogeneity-Examples of Sand Lenses in Tills

Timo Christian Kessler, Alessandro Comunian, Fabio Oriani, Philippe Renard, Bertel Nilsson, Knud Erik Klint, Poul Løgstrup Bjerg
2012 Ground Water  
We used one cross-section to parameterize the spatial correlation and a second, parallel section as a reference: it allowed testing the quality of the simulations as a function of the amount of conditioning  ...  One can use a technique that consists in splitting the 3D domain into a set of slices in various directions that are sequentially simulated and reassembled into a 3D block.  ...  Survey of Denmark and Greenland (GEUS) and REMTEC, Innovative REMediation and assessment TEChnologies for contaminated soil and groundwater, Danish Council for Strategic Research.  ... 
doi:10.1111/j.1745-6584.2012.01015.x pmid:23252428 fatcat:a6ununrnknbftkwq6f7heriflm

Geostatistical Methods for Lithological Aquifer Characterization and Groundwater Flow Modeling of the Catania Plain Quaternary Aquifer (Italy)

Enrico Guastaldi, Andrea Carloni, Giovanna Pappalardo, Jacopo Nevini
2014 Journal of Water Resource and Protection  
Transition probabilities based on a Markov Chain (MC) and Sequential Indicator Simulation (SIS) are the structure-imitating simulators utilized for generating stochastic distributions of hydraulic conductivity  ...  fields of CPQA, basing on borehole data: plausible equiprobable solutions of the complex geological structure of the CPQA were simulated.  ...  Gianluca Cornamusini (CGT Center for GeoTechnologies-University of Siena, Italy), which helped them in several parts of this work.  ... 
doi:10.4236/jwarp.2014.64032 fatcat:l7kvbtibhrhznlhplqqoc2nivy

Simultaneous Estimation of Geologic and Reservoir State Variables Within an Ensemble-Based Multiple-Point Statistic Framework

Liangping Li, Sanjay Srinivasan, Haiyan Zhou, J. Jaime Gómez-Hernández
2013 Mathematical Geosciences  
field using the dynamic data at the next instant, without running expensive flow simulations.  ...  The methodology is an extension of a previously developed pattern search-based inverse method that models the spatial variation in flow parameters by searching for patterns in an ensemble of reservoir  ...  The authors also wish to thank the guest editors Philippe Renard and Grégoire Mariethoz as well as three anonymous reviewers for their comments, which substantially helped improving the final version of  ... 
doi:10.1007/s11004-013-9504-z fatcat:2unjj54adjhufbzxlphjmlizrq

Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences [chapter]

K. G. van den Boogaart, R. Tolosana-Delgado
2018 Handbook of Mathematical Geosciences  
This solution heavily relies on all classical fields of mathematical geosciences and geoinformatics, but requires new mathematical and computational developments.  ...  This chapter describes the state of the art and the mathematical building blocks of a possible solution to this problem.  ...  Nowadays, point and block kriging or simulation for grade variables and indicator-based techniques (indicator kriging, sequential indicator simulation, plurigaussian simulation) for categorical variables  ... 
doi:10.1007/978-3-319-78999-6_33 fatcat:m42wu4gngfal7n5qayhhhcachq

Contaminant Source Identification from Finite Sensor Data: Perron–Frobenius Operator and Bayesian Inference

Himanshu Sharma, Umesh Vaidya, Baskar Ganapathysubramanian
2021 Energies  
The method provides a fast, accurate, and efficient framework for real-time identification of contaminant source location.  ...  The discrete PF operator provides a fast, effective and accurate model for contaminant transport modeling.  ...  (a) The geometry used in the 3D room is used for the construction of the Markov matrix. (b) The computational mesh is used for performing the CFD for computing the flow field in the room.  ... 
doi:10.3390/en14206729 fatcat:lhhj3b42x5bwbb3bmjliprcqyi

Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches

Tiancheng Li, Shudong Sun, Tariq Pervez Sattar, Juan Manuel Corchado
2014 Expert systems with applications  
This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters.  ...  These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony  ...  Nevertheless, to find more effective solutions for PDO in high dimensional problems remains an active field and there is still much room to go.  ... 
doi:10.1016/j.eswa.2013.12.031 fatcat:3hxwv3eyejbyppm2je64yfs3cm
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